Part of why I hate the term "insights" as a term for what data teams produce is that it's not the early 2010's anymore--no one cares about artisans producing bespoke, handcrafted things
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DS and analyst work IS contributing a system. That system might be more abstract than an app or website, but the work of one DS or analyst are connected to each other and can absolutely have an impact on the work of their colleagues
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SWE is scaled by trying to automate infrastructure coordination, management, and risk. Infrastructure is to SWE as data is to DS. Not managing your data in an automated way invites coordination costs and risk, and it naturally leads you into the insights trap.
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Btw, if it seems like I'm talking about what makes SWE teams effective recently, it's because it's part of my job! If you'd like it to be your job too, I'm currently on the lookout for a tech lead :)https://smrtr.io/5BvSG
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$bigco I think this is where collaboration with DE and Data Infra SWE is key. Ladders of abstraction layered on the same graph of facts and relationships, DS links data narratives, DE Tables and Pipelines, DI underlying systems.Thanks. Twitter will use this to make your timeline better. UndoUndo
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